skip to main content

Title: Changes in the Vaginal Microbiome and Associated Toxicities Following Radiation Therapy for Gynecologic Cancers
Postmenopausal women often suffer from vaginal symptoms associated with atrophic vaginitis. Additionally, gynecologic cancer survivors may live for decades with additional, clinically significant, persistent vaginal toxicities caused by cancer therapies, including pain, dyspareunia, and sexual dysfunction. The vaginal microbiome (VM) has been previously linked with vaginal symptoms related to menopause ( i.e. dryness). Our previous work showed that gynecologic cancer patients exhibit distinct VM profiles from healthy women, with low abundance of lactobacilli and prevalence of multiple opportunistic pathogenic bacteria. Here we explore the association between the dynamics and structure of the vaginal microbiome with the manifestation and persistence of vaginal symptoms, during one year after completion of cancer therapies, while controlling for clinical and sociodemographic factors. We compared cross-sectionally the vaginal microbiome in 134 women, 64 gynecologic patients treated with radiotherapy and 68 healthy controls, and we longitudinally followed a subset of 52 women quarterly (4 times in a year: pre-radiation therapy, 2, 6 and 12 months post-therapy). Differences among the VM profiles of cancer and healthy women were more pronounced with the progression of time. Cancer patients had higher diversity VMs and a variety of vaginal community types (CTs) that are not dominated by Lactobacilli , with extensive more » VM variation between individuals. Additionally, cancer patients exhibit highly unstable VMs (based on Bray-Curtis distances) compared to healthy controls. Vaginal symptoms prevalent in cancer patients included vaginal pain (40%), hemorrhage (35%), vaginismus (28%) and inflammation (20%), while symptoms such as dryness (45%), lack of lubrication (33%) and dyspareunia (32%) were equally or more prominent in healthy women at baseline. However, 24% of cancer patients experienced persistent symptoms at all time points, as opposed to 12% of healthy women. Symptom persistence was strongly inversely correlated with VM stability; for example, patients with persistent dryness or abnormally high pH have the most unstable microbiomes. Associations were identified between vaginal symptoms and individual bacterial taxa, including: Prevotella with vaginal dryness, Delftia with pain following vaginal intercourse, and Gemillaceaea with low levels of lubrication during intercourse. Taken together our results indicate that gynecologic cancer therapy is associated with reduced vaginal microbiome stability and vaginal symptom persistence. « less
Authors:
; ; ; ; ; ; ; ; ; ;
Award ID(s):
1759831
Publication Date:
NSF-PAR ID:
10354258
Journal Name:
Frontiers in Cellular and Infection Microbiology
Volume:
11
ISSN:
2235-2988
Sponsoring Org:
National Science Foundation
More Like this
  1. The gut is a well-established route of infection and target for viral damage by SARSCoV-2. This is supported by the clinical observation that about half of COVID-19 patients exhibit gastrointestinal (GI) symptoms. We asked whether the analysis of plasma could provide insight into gut barrier dysfunction in patients with COVID-19 infection. Plasma samples of COVID-19 patients (n=30) and healthy control (n=16) were collected during hospitalization. Plasma microbiome was analyzed using 16S rRNA sequencing, metatranscriptomic analysis, and gut permeability markers including FABP-2, PGN and LPS in both patient cohorts. Almost 65% (9 out 14) COVID-19 patients showed abnormal presence of gut microbes in their bloodstream. Plasma samples contained predominately Proteobacteria, Firmicutes, and Actinobacteria. The abundance of gram-negative bacteria (Acinetobacter, Nitrospirillum, Cupriavidus, Pseudomonas, Aquabacterium, Burkholderia, Caballeronia, Parabhurkholderia, Bravibacterium, and Sphingomonas) was higher than the gram-positive bacteria (Staphylococcus and Lactobacillus) in COVID-19 subjects. The levels of plasma gut permeability markers FABP2 (1282±199.6 vs 838.1±91.33; p=0.0757), PGN (34.64±3.178 vs 17.53±2.12; p<0.0001), and LPS (405.5±48.37 vs 249.6±17.06; p=0.0049) were higher in COVID-19 patients compared to healthy subjects. These findings support that the intestine may represent a source for bacteremia and may contribute to worsening COVID-19 outcomes. Therapies targeting the gut and prevention of gut barriermore »defects may represent a strategy to improve outcomes in COVID19 patients.« less
  2. Abstract

    Irritable bowel syndrome (IBS) is the most prevalent disorder of brain-gut interactions that affects between 5 and 10% of the general population worldwide. The current symptom criteria restrict the diagnosis to recurrent abdominal pain associated with altered bowel habits, but the majority of patients also report non-painful abdominal discomfort, associated psychiatric conditions (anxiety and depression), as well as other visceral and somatic pain-related symptoms. For decades, IBS was considered an intestinal motility disorder, and more recently a gut disorder. However, based on an extensive body of reported information about central, peripheral mechanisms and genetic factors involved in the pathophysiology of IBS symptoms, a comprehensive disease model of brain-gut-microbiome interactions has emerged, which can explain altered bowel habits, chronic abdominal pain, and psychiatric comorbidities. In this review, we will first describe novel insights into several key components of brain-gut microbiome interactions, starting with reported alterations in the gut connectome and enteric nervous system, and a list of distinct functional and structural brain signatures, and comparing them to the proposed brain alterations in anxiety disorders. We will then point out the emerging correlations between the brain networks with the genomic, gastrointestinal, immune, and gut microbiome-related parameters. We will incorporate this newmore »information into a systems-based disease model of IBS. Finally, we will discuss the implications of such a model for the improved understanding of the disorder and the development of more effective treatment approaches in the future.

    « less
  3. Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standardmore »autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.« less
  4. Abstract Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians’ ability to accurately predict a specific patient’s eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% ( p  = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% ( p  = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were highermore »than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.« less
  5. Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basalmore »subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.« less